This invention relates to systems for object recognition and more particularly, to systems for extracting features related to the shape of objects in a two dimensional image.
Automated object recognition systems have been developed with increasing sophistication in step with the development of data processing techniques and related equipment. Such systems have been applied to object recognition problems arising in a large variety of fields including biomedicine, metallurgy, cartography, character recognition, and industrial automation. Many early systems were directed to particle counting and did little more than identify and count individual objects from a group presented to a sensor. With improved technology, the prior art system capabilities have advanced to include line or contour following, pattern matching, and object shape feature extraction for elementary shape features, such as area, perimeter, moments. Much of the prior art image analysis effort has been directed to systems which detect binary characteristics of cellular regions in an image and compare regional sets of those characteristics with a predetermined set of image sub-patterns or masks. Each of the sometimes complex masks may be effectively scanned over a digital representation of the image so that a common characteristic of such shape feature extraction systems is the requirement for enormous computing resources. Typically, an entire image is stored in a memory while portions of that image are repeatedly scanned to identify the various shape features. The feature extraction techniques thus examine the image many times before extracting values for the shape features.
Certain prior art systems provide a computational advantage over the basic systems by encoding the stored data. Except for very simple features, however, even these systems require the lines of scan to be examined many times in order to convert a sequentially scanned image to the encoded form. This encoding process itself requires substantial computing resources. As a result of the complexity of the prior art feature extraction techniques, only the extraction of relatively simple features, such as area (A), perimeter (P) and P.sup.2 /A, is within the capability of most prior art systems.
In order to reduce the required computer resources for shape feature extraction, while still providing high speed analysis of an image, sequential processing techniques have been developed so that certain features may be extracted during a single scan of an image. See Rosenfeld, A., Picture Processing By Compunter, Academic Press, New York, 1962. Such techniques include the extraction of area, perimeter, and moments. U.S. Pat. No. 3,408,485 illustrates a sequential technique for identifying and counting objects in an image by first identifying portions of objects recurring in successive scan lines. However, even the known sequential techniques fail to provide feature extraction which may be used for the high speed identification of complex features with a relatively low computer resource requirement.
One particular area of use for systems identifying certain shape features of objects in two dimensional images is the field of cytology, and more particularly in systems for classifying white blood cells in a sample. Particular features of interest in such analyses are cell area, perimeter, P.sup.2 /A, moments, boundary segmentation, and "neck" identification. The latter feature is particulary related to recently developed diagnostic procedures which require relative population counts of band form and polymorphonuclear neutrophils, and counts of normal and atypical lymphocytes. In such applications, white blood cells are examimed to determine their maturity level by evaluating the degree of necking (related to the number of lobes of a cell nucleus which are connected by a filament) present in a cell, including the number of such necks and the relative neck size. Although this evaluation of the white blood cells is currently becoming a powerful diagnostic tool in biomedicine, such cell analysis is presently possible only on a manual level, using operators who visually make such analysis through the use of microscopes or microscanning video systems. While prior studies have utilized computer analysis to identify neck features of image objects from identified object boundary points, these efforts generally require the analysis of such boundary points by making pair-wise distance measurements for all points on the object boundary on a non-sequential processing basis, requiring a substantially large number of computational procedures and correspondingly large computer resource. See for example, Rosenfeld, A., "Picture Processing: 1972", Computer Graphics and Image Processing, Vol. 1, 1972.
Accordingly, it is an object of the present invention to provide a system for the indentification of shape features of objects in a two dimensional image by sequentially processing data representative of the image.
Still another object is to provide a high speed and efficient system for identification of shape features of objects in a two dimensional image.